Code
library(MASS)
library(stargazer)
library(httr)
library(jsonlite)
library(knitr)
library(rgbif)
library(ggrepel)
library(extrafont)
library(tidyverse)
options(knitr.kable.NA = '')
options(scipen=999)Data analyses
The iNaturalist Network is a localised experience that is fully connected to the global iNaturalist community. Network members are local institutions that promote local use and facilitate the use of data from iNaturalist to benefit local
The aim of this report is to give an account of the importance of the iNaturalist network members by analysing the number of records for each country.
library(MASS)
library(stargazer)
library(httr)
library(jsonlite)
library(knitr)
library(rgbif)
library(ggrepel)
library(extrafont)
library(tidyverse)
options(knitr.kable.NA = '')
options(scipen=999)iNat_network <-
tribble(~'site', ~'site_name', ~'site_id',
'Global', 'iNaturalist', 1,
'Mexico', 'iNaturalistMX', 2,
'New Zealand', 'iNaturalistNZ', 3,
'Canada', 'iNaturalist.ca', 5,
'Colombia', 'NaturalistaCO', 6,
'Portugal', 'BioDiversity4All', 8,
'Australia', 'iNaturalistAU', 9,
'Panama', 'iNaturalistPa', 13,
'Ecuador', 'iNaturalistEc', 14,
'Israel', 'iNaturalistil', 15,
'Argentina', 'ArgentiNat', 16,
'Costa Rica', 'NaturalistaCR', 17,
'Chile', 'iNaturalistCL', 18,
'Finland', 'iNaturalistFi', 20,
'Sweeden', 'iNaturalist.Se', 21,
'Spain', 'Natusfera', 22,
'Greece', 'iNaturalistGR', 23,
'Guatemala', 'iNaturalistGT', 24,
'United Kingdom', 'iNaturalistUK', 25,
'Luxembourg', 'iNaturalist.LU', 26,
'Taiwan', 'iNaturalistTW', 27,
'Uruguay', 'NaturalistaUY', 28)
iNat_network %>%
mutate('#'= row_number()) %>% relocate('#') %>%
rename(`Site` = site,
`Name`=site_name,
`ID`=site_id) %>%
kableExtra::kbl(digits=1, format.args = list(big.mark = ',')) %>%
kableExtra::kable_material('striped') %>%
kableExtra::row_spec(row = c(2,5,8,9,11,12,13,18,22), bold = T, color = "white", background = "#228A22")| # | Site | Name | ID |
|---|---|---|---|
| 1 | Global | iNaturalist | 1 |
| 2 | Mexico | iNaturalistMX | 2 |
| 3 | New Zealand | iNaturalistNZ | 3 |
| 4 | Canada | iNaturalist.ca | 5 |
| 5 | Colombia | NaturalistaCO | 6 |
| 6 | Portugal | BioDiversity4All | 8 |
| 7 | Australia | iNaturalistAU | 9 |
| 8 | Panama | iNaturalistPa | 13 |
| 9 | Ecuador | iNaturalistEc | 14 |
| 10 | Israel | iNaturalistil | 15 |
| 11 | Argentina | ArgentiNat | 16 |
| 12 | Costa Rica | NaturalistaCR | 17 |
| 13 | Chile | iNaturalistCL | 18 |
| 14 | Finland | iNaturalistFi | 20 |
| 15 | Sweeden | iNaturalist.Se | 21 |
| 16 | Spain | Natusfera | 22 |
| 17 | Greece | iNaturalistGR | 23 |
| 18 | Guatemala | iNaturalistGT | 24 |
| 19 | United Kingdom | iNaturalistUK | 25 |
| 20 | Luxembourg | iNaturalist.LU | 26 |
| 21 | Taiwan | iNaturalistTW | 27 |
| 22 | Uruguay | NaturalistaUY | 28 |
We tested different explanatory variables and saw which is the model that best explains the values a country has for iNaturalist.
Response variables:
n_records_inat.n_records_gbif_iNat.n_users.Explanatory variables:
population.area.latitude.hdi.gdp_per_capita.gdp_research.Functions
source('R/national_sites.R')Data download
America <- tibble(country_name= c('Canada', 'Mexico', 'Brazil', 'Costa Rica', 'Colombia', 'Peru', 'Argentina', 'Ecuador', 'Panama', 'Chile', 'Venezuela', 'Belize', 'Honduras', 'Bolivia', 'Guatemala', 'Cuba', 'Nicaragua', 'Paraguay', 'Bahamas', 'Jamaica', 'Trinidad and Tobago', 'Guyana', 'Dominican Republic', 'El Salvador', 'Suriname', 'Uruguay', 'Haiti'))
America <- America %>%
mutate(country_code = countrycode::countrycode(country_name,
origin = 'country.name',
destination = 'iso2c'))
America <- left_join(America, iNat_network %>% rename(country_name=site))
n_inat_gbif_country <- recordsPerCountryGBIF(America$country_code)
n_inat_country <- recordsPerCountryiNat(America$country_name)
n_users_country <- usersPerCountryiNat(America$country_name)
area_country <-areaPerCountry(America$country_code)
population <- populationPerCountry(America$country_code)
gdp_per_capita <- gdpPerCapitaCountry(America$country_code)
gdp_research <- gdpResearchPerCountry(America$country_code)
latitude <- latitudePerCountry(America$country_code)
data_variables_America <- left_join(left_join(left_join(
left_join(left_join(left_join(left_join(left_join(
America, n_inat_gbif_country),
n_inat_country),
n_users_country),
area_country),
population),
gdp_per_capita),
gdp_research), latitude)
saveRDS(data_variables_America, 'data/America_data_variables.rds')
########################################################################
Europe <- tibble(country_name = c('Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus', 'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Greece', 'Hungary', 'Ireland', 'Italy', 'Latvia', 'Lithuania', 'Luxembourg', 'Malta', 'Netherlands', 'Poland', 'Portugal', 'Romania', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'United Kingdom', 'Iceland', 'Liechtenstein', 'Norway', 'Switzerland', 'Albania', 'Bosnia and Herzegovina', 'Georgia', 'Moldova', 'Montenegro', 'Macedonia', 'Serbia', 'Turkey', 'Ukraine'))
Europe <- Europe %>%
mutate(country_code = countrycode::countrycode(country_name,
origin = 'country.name',
destination = 'iso2c'))
Europe <- left_join(Europe, iNat_network %>% rename(country_name=site))
n_inat_gbif_country <- recordsPerCountryGBIF(Europe$country_code)
n_inat_country <- recordsPerCountryiNat(Europe$country_name)
n_users_country <- usersPerCountryiNat(Europe$country_name)
area_country <-areaPerCountry(Europe$country_code)
population <- populationPerCountry(Europe$country_code)
gdp_per_capita <- gdpPerCapitaCountry(Europe$country_code)
gdp_research <- gdpResearchPerCountry(Europe$country_code)
latitude <- latitudePerCountry(Europe$country_code)
data_variables_Europe <- left_join(left_join(left_join(
left_join(left_join(left_join(left_join(left_join(
Europe, n_inat_gbif_country),
n_inat_country),
n_users_country),
area_country),
population),
gdp_per_capita),
gdp_research), latitude)
saveRDS(data_variables_Europe, 'data/Europe_data_variables.rds')
########################################################################
Asia <- tibble(country_name = c('India', 'China', 'Indonesia', 'Pakistan', 'Bangladesh', 'Japan', 'Philippines', 'Vietnam', 'Iran', 'Turkey', 'Thailand', 'Myanmar', 'South Korea','Iraq', 'Afghanistan', 'Yemen', 'Uzbekistan', 'Malaysia', 'Saudi Arabia', 'Nepal', 'North Korea','Syria', 'Sri Lanka','Kazakhstan', 'Cambodia', 'Jordan', 'United Arab Emirates', 'Tajikistan', 'Azerbaijan', 'Israel', 'Laos', 'Turkmenistan', 'Kyrgyzstan', 'Singapore', 'Lebanon', 'Palestine','Oman', 'Kuwait', 'Georgia', 'Mongolia', 'Qatar', 'Armenia', 'Bahrain', 'Timor Leste', 'Cyprus', 'Bhutan', 'Maldives', 'Brunei', 'Taiwan'))
Asia <- Asia %>%
mutate(country_code = countrycode::countrycode(country_name,
origin = 'country.name',
destination = 'iso2c'))
Asia <- left_join(Asia, iNat_network %>% rename(country_name=site))
n_inat_gbif_country <- recordsPerCountryGBIF(Asia$country_code)
n_inat_country <- recordsPerCountryiNat(Asia$country_name)
n_users_country <- usersPerCountryiNat(Asia$country_name)
area_country <-areaPerCountry(Asia$country_code)
population <- populationPerCountry(Asia$country_code)
gdp_per_capita <- gdpPerCapitaCountry(Asia$country_code)
gdp_research <- gdpResearchPerCountry(Asia$country_code)
latitude <- latitudePerCountry(Asia$country_code)
data_variables_Asia <- left_join(left_join(left_join(
left_join(left_join(left_join(left_join(left_join(
Asia, n_inat_gbif_country),
n_inat_country),
n_users_country),
area_country),
population),
gdp_per_capita),
gdp_research), latitude)
data_variables_Asia <- data_variables_Asia %>%
mutate(area = ifelse(country_name == 'Taiwan', 36197, area),
pop = ifelse(country_name == 'Taiwan', 23365274, pop))
saveRDS(data_variables_Asia, 'data/Asia_data_variables.rds')
########################################################################
Oceania <- tibble(country_name = c('Australia', 'Papua New Guinea', 'New Zealand', 'Fiji', 'Solomon Islands', 'Federated States of Micronesia', 'Vanuatu', 'Samoa', 'Kiribati', 'Tonga', 'Marshall Islands', 'Palau', 'Tuvalu', 'Nauru'))
Oceania <- Oceania %>%
mutate(country_code = countrycode::countrycode(country_name,
origin = 'country.name',
destination = 'iso2c'))
Oceania <- left_join(Oceania, iNat_network %>% rename(country_name=site))
n_inat_gbif_country <- recordsPerCountryGBIF(Oceania$country_code)
n_inat_country <- recordsPerCountryiNat(Oceania$country_name)
n_users_country <- usersPerCountryiNat(Oceania$country_name)
area_country <-areaPerCountry(Oceania$country_code)
population <- populationPerCountry(Oceania$country_code)
gdp_per_capita <- gdpPerCapitaCountry(Oceania$country_code)
gdp_research <- gdpResearchPerCountry(Oceania$country_code)
latitude <- latitudePerCountry(Oceania$country_code)
data_variables_Oceania <- left_join(left_join(left_join(
left_join(left_join(left_join(left_join(left_join(
Oceania, n_inat_gbif_country),
n_inat_country),
n_users_country),
area_country),
population),
gdp_per_capita),
gdp_research), latitude)
saveRDS(data_variables_Oceania, 'data/Oceania_data_variables.rds')
########################################################################
variables_global <- bind_rows(data_variables_America %>%
mutate(continent = 'America'),
data_variables_Europe %>%
mutate(continent = 'Europe'),
data_variables_Asia %>%
mutate(continent = 'Asia'),
data_variables_Oceania %>%
mutate(continent = 'Oceania')) %>%
unique()
variables_global <- bind_rows(readRDS('data/America_data_variables.rds') %>%
mutate(continent = 'America'),
readRDS('data/Europe_data_variables.rds') %>%
mutate(continent = 'Europe'),
readRDS('data/Asia_data_variables.rds') %>%
mutate(continent = 'Asia'),
readRDS('data/Oceania_data_variables.rds') %>%
mutate(continent = 'Oceania')) %>%
unique()
saveRDS(variables_global, 'data/global_data_variables.rds')ids <- data_variables %>%
select(country_name,site_name, n_records_inat) %>%
arrange(desc(n_records_inat)) %>%
with(which(!is.na(site_name)))
data_variables %>%
select(country_name,site_name, n_records_inat) %>%
arrange(desc(n_records_inat)) %>%
mutate('#'= row_number()) %>% relocate('#') %>%
rename(`Country` = country_name,
`iNat site` = site_name,
`Records on iNat`=n_records_inat) %>%
kableExtra::kbl(digits=4, format.args = list(big.mark = ',')) %>%
kableExtra::kable_material('striped') %>%
kableExtra::row_spec(ids, bold = T, color = "white", background = "#228A22") %>%
kableExtra::scroll_box(height = '600px')| # | Country | iNat site | Records on iNat |
|---|---|---|---|
| 1 | Canada | iNaturalist.ca | 17,900,009 |
| 2 | Australia | iNaturalistAU | 10,849,285 |
| 3 | Mexico | iNaturalistMX | 8,412,995 |
| 4 | United Kingdom | iNaturalistUK | 6,943,926 |
| 5 | Germany | 5,091,498 | |
| 6 | France | 4,932,213 | |
| 7 | Spain | Natusfera | 4,604,659 |
| 8 | Taiwan | iNaturalistTW | 3,666,074 |
| 9 | Italy | 3,662,680 | |
| 10 | India | 3,571,023 | |
| 11 | Brazil | 3,496,740 | |
| 12 | New Zealand | iNaturalistNZ | 2,981,047 |
| 13 | Austria | 2,476,537 | |
| 14 | Portugal | BioDiversity4All | 2,107,143 |
| 15 | Colombia | NaturalistaCO | 1,907,160 |
| 16 | Ecuador | iNaturalistEc | 1,874,438 |
| 17 | Argentina | ArgentiNat | 1,833,740 |
| 18 | Denmark | 1,657,667 | |
| 19 | China | 1,646,241 | |
| 20 | Costa Rica | NaturalistaCR | 1,580,872 |
| 21 | Ukraine | 1,526,147 | |
| 22 | Czechia | 1,301,754 | |
| 23 | Poland | 1,254,562 | |
| 24 | Finland | iNaturalistFi | 1,170,974 |
| 25 | Thailand | 996,118 | |
| 26 | Malaysia | 973,288 | |
| 27 | Japan | 965,864 | |
| 28 | Indonesia | 955,846 | |
| 29 | Netherlands | 954,360 | |
| 30 | Bolivia | 940,781 | |
| 31 | Switzerland | 905,895 | |
| 32 | Chile | iNaturalistCL | 874,666 |
| 33 | Peru | 785,071 | |
| 34 | Singapore | 746,405 | |
| 35 | Panama | iNaturalistPa | 716,270 |
| 36 | Greece | iNaturalistGR | 709,163 |
| 37 | Sweden | 659,853 | |
| 38 | Belgium | 647,042 | |
| 39 | South Korea | 532,394 | |
| 40 | Hungary | 481,094 | |
| 41 | Philippines | 479,525 | |
| 42 | Lithuania | 464,960 | |
| 43 | Croatia | 427,593 | |
| 44 | Luxembourg | iNaturalist.LU | 375,167 |
| 45 | Norway | 371,783 | |
| 46 | Turkey | 367,680 | |
| 47 | Turkey | 367,680 | |
| 48 | Israel | iNaturalistil | 358,153 |
| 49 | Honduras | 333,491 | |
| 50 | Ireland | 263,299 | |
| 51 | Romania | 247,772 | |
| 52 | Slovakia | 226,580 | |
| 53 | Slovenia | 205,635 | |
| 54 | Sri Lanka | 203,493 | |
| 55 | Kazakhstan | 198,800 | |
| 56 | Vietnam | 189,863 | |
| 57 | Guatemala | iNaturalistGT | 169,504 |
| 58 | Bulgaria | 162,215 | |
| 59 | Belize | 159,063 | |
| 60 | Uruguay | NaturalistaUY | 156,551 |
| 61 | Dominican Republic | 145,886 | |
| 62 | Serbia | 144,065 | |
| 63 | Trinidad and Tobago | 127,655 | |
| 64 | Nicaragua | 122,378 | |
| 65 | Iceland | 115,563 | |
| 66 | Nepal | 110,868 | |
| 67 | Mongolia | 94,205 | |
| 68 | El Salvador | 92,405 | |
| 69 | Venezuela | 89,298 | |
| 70 | Cuba | 87,892 | |
| 71 | Cambodia | 83,930 | |
| 72 | Estonia | 75,483 | |
| 73 | Jamaica | 73,804 | |
| 74 | Albania | 73,194 | |
| 75 | Armenia | 73,171 | |
| 76 | United Arab Emirates | 71,277 | |
| 77 | Latvia | 69,627 | |
| 78 | Fiji | 66,619 | |
| 79 | Cyprus | 65,867 | |
| 80 | Maldives | 61,266 | |
| 81 | Bahamas | 59,704 | |
| 82 | Iran | 59,489 | |
| 83 | Montenegro | 57,086 | |
| 84 | Bhutan | 46,283 | |
| 85 | Uzbekistan | 42,320 | |
| 86 | Bosnia and Herzegovina | 41,590 | |
| 87 | Pakistan | 36,960 | |
| 88 | Paraguay | 35,851 | |
| 89 | Bangladesh | 35,612 | |
| 90 | Papua New Guinea | 35,464 | |
| 91 | Kyrgyzstan | 33,781 | |
| 92 | Myanmar | 32,490 | |
| 93 | Palestine | 31,040 | |
| 94 | Saudi Arabia | 30,342 | |
| 95 | Oman | 29,897 | |
| 96 | Suriname | 29,312 | |
| 97 | Kuwait | 29,189 | |
| 98 | Malta | 27,991 | |
| 99 | Guyana | 27,703 | |
| 100 | Macedonia | 27,626 | |
| 101 | Laos | 26,952 | |
| 102 | Syria | 24,597 | |
| 103 | Marshall Islands | 24,568 | |
| 104 | Vanuatu | 23,271 | |
| 105 | Jordan | 22,029 | |
| 106 | Palau | 18,512 | |
| 107 | Azerbaijan | 18,467 | |
| 108 | Haiti | 16,634 | |
| 109 | Solomon Islands | 14,516 | |
| 110 | Moldova | 14,322 | |
| 111 | Iraq | 14,125 | |
| 112 | Lebanon | 13,122 | |
| 113 | Brunei | 11,386 | |
| 114 | Cyprus | 10,894 | |
| 115 | Qatar | 10,678 | |
| 116 | Yemen | 9,808 | |
| 117 | Tajikistan | 9,560 | |
| 118 | Tonga | 8,175 | |
| 119 | Georgia | 6,820 | |
| 120 | Georgia | 6,820 | |
| 121 | Federated States of Micronesia | 6,153 | |
| 122 | Samoa | 5,247 | |
| 123 | Liechtenstein | 4,717 | |
| 124 | Bahrain | 3,592 | |
| 125 | Tuvalu | 2,885 | |
| 126 | Kiribati | 2,550 | |
| 127 | North Korea | 1,884 | |
| 128 | Afghanistan | 1,050 | |
| 129 | Turkmenistan | 626 | |
| 130 | Nauru | 103 | |
| 131 | Timor Leste |
ids <- data_variables %>%
select(country_name, site_name, n_records_gbif, n_records_gbif_iNat) %>%
mutate(proportion=n_records_gbif_iNat*100/n_records_gbif) %>%
arrange(desc(proportion)) %>%
with(which(!is.na(site_name)))
data_variables %>%
select(country_name, site_name, n_records_gbif, n_records_gbif_iNat) %>%
mutate(proportion=n_records_gbif_iNat*100/n_records_gbif) %>%
arrange(desc(proportion)) %>%
mutate('#'= row_number()) %>% relocate('#') %>%
select(-n_records_gbif_iNat) %>%
rename(`Country` = country_name,
`iNat site` = site_name,
`Records from iNat on GBIF`=n_records_gbif,
`Proportion`=proportion) %>%
kableExtra::kbl(digits=4, format.args = list(big.mark = ',')) %>%
kableExtra::kable_material('striped') %>%
kableExtra::row_spec(ids, bold = T, color = "white", background = "#228A22") %>%
kableExtra::scroll_box(height = '600px')| # | Country | iNat site | Records from iNat on GBIF | Proportion |
|---|---|---|---|---|
| 1 | Maldives | 102,600 | 28.5780 | |
| 2 | Ukraine | 3,305,090 | 27.4630 | |
| 3 | Albania | 113,283 | 26.0198 | |
| 4 | Italy | 7,401,549 | 22.7511 | |
| 5 | Singapore | 1,664,886 | 22.4301 | |
| 6 | Kazakhstan | 435,373 | 22.2200 | |
| 7 | Montenegro | 142,401 | 19.2155 | |
| 8 | Tuvalu | 9,554 | 18.7565 | |
| 9 | Croatia | 1,078,021 | 18.6975 | |
| 10 | Malta | 81,373 | 18.3243 | |
| 11 | Lithuania | 1,232,602 | 17.2011 | |
| 12 | Bosnia and Herzegovina | 82,290 | 16.6764 | |
| 13 | Uzbekistan | 117,367 | 15.7259 | |
| 14 | Fiji | 275,858 | 15.1440 | |
| 15 | Marshall Islands | 109,599 | 14.1853 | |
| 16 | Hungary | 1,882,300 | 12.8489 | |
| 17 | New Zealand | iNaturalistNZ | 14,957,900 | 12.3202 |
| 18 | Czechia | 4,357,996 | 12.2621 | |
| 19 | Greece | iNaturalistGR | 3,065,179 | 12.0781 |
| 20 | Indonesia | 3,076,242 | 11.8549 | |
| 21 | Austria | 12,772,943 | 11.5651 | |
| 22 | Timor Leste | 91,370 | 11.3823 | |
| 23 | Slovenia | 790,292 | 11.2033 | |
| 24 | Armenia | 192,968 | 11.1687 | |
| 25 | Romania | 1,070,706 | 10.6347 | |
| 26 | Mexico | iNaturalistMX | 31,246,248 | 10.4663 |
| 27 | Iraq | 72,541 | 8.8419 | |
| 28 | Macedonia | 118,106 | 8.1918 | |
| 29 | Malaysia | 3,341,240 | 8.0139 | |
| 30 | Mongolia | 571,726 | 7.9358 | |
| 31 | Cyprus | 522,317 | 7.5020 | |
| 32 | Cyprus | 522,489 | 7.4995 | |
| 33 | Bahrain | 22,772 | 7.4697 | |
| 34 | Argentina | ArgentiNat | 14,442,370 | 7.3516 |
| 35 | Serbia | 865,944 | 7.2042 | |
| 36 | Dominican Republic | 784,391 | 7.1786 | |
| 37 | Slovakia | 1,688,147 | 7.0103 | |
| 38 | Taiwan | iNaturalistTW | 21,421,162 | 6.9228 |
| 39 | Kyrgyzstan | 203,073 | 6.7690 | |
| 40 | Yemen | 91,962 | 6.7506 | |
| 41 | Vietnam | 783,215 | 6.7150 | |
| 42 | Jordan | 140,953 | 6.5376 | |
| 43 | Brunei | 49,391 | 6.4060 | |
| 44 | Vanuatu | 165,949 | 6.0513 | |
| 45 | Luxembourg | iNaturalist.LU | 3,354,337 | 5.9892 |
| 46 | Philippines | 2,079,909 | 5.8888 | |
| 47 | Thailand | 6,122,726 | 5.7396 | |
| 48 | Japan | 8,472,177 | 5.1061 | |
| 49 | Palau | 193,504 | 5.1053 | |
| 50 | Canada | iNaturalist.ca | 178,493,991 | 5.1031 |
| 51 | Solomon Islands | 185,161 | 5.0497 | |
| 52 | Portugal | BioDiversity4All | 19,715,797 | 5.0231 |
| 53 | Uruguay | NaturalistaUY | 1,681,236 | 4.9915 |
| 54 | China | 9,433,222 | 4.9189 | |
| 55 | Bulgaria | 1,835,892 | 4.7743 | |
| 56 | Samoa | 45,966 | 4.7731 | |
| 57 | Sri Lanka | 2,146,804 | 4.4949 | |
| 58 | Trinidad and Tobago | 1,127,557 | 4.4920 | |
| 59 | Ecuador | iNaturalistEc | 11,650,113 | 4.4398 |
| 60 | Georgia | 1,181,861 | 4.3536 | |
| 61 | Georgia | 1,181,992 | 4.3531 | |
| 62 | Germany | 62,252,275 | 4.3359 | |
| 63 | Australia | iNaturalistAU | 135,376,182 | 4.1283 |
| 64 | Turkey | 3,309,188 | 4.1221 | |
| 65 | Turkey | 3,309,436 | 4.1218 | |
| 66 | Bolivia | 2,200,010 | 4.1167 | |
| 67 | Myanmar | 333,389 | 4.0925 | |
| 68 | Poland | 14,780,502 | 4.0749 | |
| 69 | Latvia | 604,673 | 4.0703 | |
| 70 | Jamaica | 680,756 | 3.9274 | |
| 71 | Moldova | 121,784 | 3.8347 | |
| 72 | Haiti | 189,174 | 3.7553 | |
| 73 | Brazil | 26,616,164 | 3.7527 | |
| 74 | Kuwait | 311,359 | 3.6989 | |
| 75 | Qatar | 135,904 | 3.6460 | |
| 76 | South Korea | 5,403,467 | 3.6406 | |
| 77 | Laos | 242,832 | 3.5642 | |
| 78 | Iran | 818,764 | 3.4275 | |
| 79 | Chile | iNaturalistCL | 10,730,394 | 3.3230 |
| 80 | Azerbaijan | 242,165 | 3.2565 | |
| 81 | Honduras | 3,459,120 | 3.2295 | |
| 82 | Tonga | 133,783 | 3.1147 | |
| 83 | Lebanon | 147,284 | 3.0723 | |
| 84 | Tajikistan | 84,825 | 3.0380 | |
| 85 | Bahamas | 987,306 | 3.0325 | |
| 86 | Spain | Natusfera | 72,566,816 | 2.9929 |
| 87 | Cambodia | 950,623 | 2.9868 | |
| 88 | Syria | 310,449 | 2.9493 | |
| 89 | Panama | iNaturalistPa | 7,850,521 | 2.9067 |
| 90 | Cuba | 1,845,461 | 2.8590 | |
| 91 | Palestine | 581,583 | 2.8202 | |
| 92 | Suriname | 463,888 | 2.7455 | |
| 93 | Saudi Arabia | 501,270 | 2.7037 | |
| 94 | Nicaragua | 2,045,924 | 2.6963 | |
| 95 | North Korea | 47,941 | 2.6950 | |
| 96 | El Salvador | 1,250,974 | 2.6841 | |
| 97 | Iceland | 2,138,710 | 2.6692 | |
| 98 | Liechtenstein | 86,720 | 2.5023 | |
| 99 | Nepal | 1,290,694 | 2.4486 | |
| 100 | Oman | 523,644 | 2.4125 | |
| 101 | Peru | 8,881,610 | 2.3764 | |
| 102 | Ireland | 5,073,295 | 2.2550 | |
| 103 | Bhutan | 564,497 | 2.2252 | |
| 104 | Bangladesh | 653,738 | 2.0881 | |
| 105 | United Kingdom | iNaturalistUK | 179,386,804 | 2.0854 |
| 106 | Israel | iNaturalistil | 7,023,671 | 2.0637 |
| 107 | India | 50,945,385 | 2.0362 | |
| 108 | Federated States of Micronesia | 130,557 | 2.0068 | |
| 109 | Costa Rica | NaturalistaCR | 31,581,986 | 1.8071 |
| 110 | Switzerland | 28,174,048 | 1.6532 | |
| 111 | Pakistan | 572,636 | 1.5437 | |
| 112 | Colombia | NaturalistaCO | 32,226,610 | 1.4854 |
| 113 | United Arab Emirates | 1,968,642 | 1.4806 | |
| 114 | Guatemala | iNaturalistGT | 4,598,258 | 1.2107 |
| 115 | Guyana | 830,244 | 1.2107 | |
| 116 | France | 192,598,919 | 1.2021 | |
| 117 | Kiribati | 157,257 | 1.1618 | |
| 118 | Belize | 6,646,850 | 1.0838 | |
| 119 | Turkmenistan | 22,062 | 1.0742 | |
| 120 | Denmark | 60,344,753 | 1.0540 | |
| 121 | Finland | iNaturalistFi | 45,743,721 | 1.0521 |
| 122 | Papua New Guinea | 1,704,726 | 0.8613 | |
| 123 | Nauru | 4,714 | 0.8061 | |
| 124 | Venezuela | 4,189,542 | 0.7974 | |
| 125 | Belgium | 39,976,677 | 0.7507 | |
| 126 | Paraguay | 1,412,769 | 0.7230 | |
| 127 | Afghanistan | 65,829 | 0.6806 | |
| 128 | Estonia | 7,446,938 | 0.5402 | |
| 129 | Netherlands | 123,944,227 | 0.3671 | |
| 130 | Norway | 53,254,830 | 0.3433 | |
| 131 | Sweden | 141,826,712 | 0.2148 |
ids <- data_variables %>%
select(country_name, site_name, n_users) %>%
arrange(desc(n_users)) %>%
with(which(!is.na(site_name)))
data_variables %>%
select(country_name, site_name, n_users) %>%
arrange(desc(n_users)) %>%
mutate('#'= row_number()) %>% relocate('#') %>%
rename(`Country` = country_name,
`iNat site` = site_name,
`Users recording on iNat`=n_users) %>%
kableExtra::kbl(digits=4, format.args = list(big.mark = ',')) %>%
kableExtra::kable_material('striped') %>%
kableExtra::row_spec(ids, bold = T, color = "white", background = "#228A22") %>%
kableExtra::scroll_box(height = '600px')| # | Country | iNat site | Users recording on iNat |
|---|---|---|---|
| 1 | Canada | iNaturalist.ca | 249,842 |
| 2 | Mexico | iNaturalistMX | 166,175 |
| 3 | United Kingdom | iNaturalistUK | 145,086 |
| 4 | France | 128,279 | |
| 5 | Australia | iNaturalistAU | 116,817 |
| 6 | Italy | 85,798 | |
| 7 | Germany | 79,925 | |
| 8 | Spain | Natusfera | 75,563 |
| 9 | Denmark | 74,527 | |
| 10 | Brazil | 69,968 | |
| 11 | Taiwan | iNaturalistTW | 57,783 |
| 12 | Colombia | NaturalistaCO | 54,068 |
| 13 | New Zealand | iNaturalistNZ | 46,991 |
| 14 | India | 46,458 | |
| 15 | Portugal | BioDiversity4All | 36,431 |
| 16 | Ecuador | iNaturalistEc | 36,348 |
| 17 | Costa Rica | NaturalistaCR | 35,332 |
| 18 | Czechia | 33,922 | |
| 19 | Finland | iNaturalistFi | 29,066 |
| 20 | Netherlands | 29,007 | |
| 21 | Bolivia | 28,962 | |
| 22 | Austria | 28,649 | |
| 23 | Argentina | ArgentiNat | 24,765 |
| 24 | Belgium | 24,564 | |
| 25 | Japan | 23,220 | |
| 26 | Thailand | 22,958 | |
| 27 | Chile | iNaturalistCL | 22,833 |
| 28 | Switzerland | 21,953 | |
| 29 | Greece | iNaturalistGR | 21,690 |
| 30 | Indonesia | 21,514 | |
| 31 | Sweden | 19,000 | |
| 32 | Malaysia | 18,659 | |
| 33 | China | 17,797 | |
| 34 | Panama | iNaturalistPa | 17,553 |
| 35 | Poland | 17,070 | |
| 36 | Peru | 16,733 | |
| 37 | Philippines | 15,739 | |
| 38 | Turkey | 13,594 | |
| 39 | Turkey | 13,594 | |
| 40 | Croatia | 12,772 | |
| 41 | Ireland | 12,725 | |
| 42 | Norway | 12,583 | |
| 43 | Ukraine | 12,060 | |
| 44 | Singapore | 9,985 | |
| 45 | Lithuania | 9,104 | |
| 46 | Honduras | 8,713 | |
| 47 | South Korea | 7,949 | |
| 48 | Guatemala | iNaturalistGT | 7,816 |
| 49 | Hungary | 7,081 | |
| 50 | Luxembourg | iNaturalist.LU | 6,843 |
| 51 | Iceland | 6,417 | |
| 52 | Israel | iNaturalistil | 6,374 |
| 53 | Slovenia | 6,300 | |
| 54 | Romania | 6,116 | |
| 55 | Dominican Republic | 6,106 | |
| 56 | Vietnam | 6,037 | |
| 57 | Slovakia | 5,431 | |
| 58 | Belize | 4,653 | |
| 59 | Sri Lanka | 4,259 | |
| 60 | United Arab Emirates | 4,161 | |
| 61 | Bahamas | 4,159 | |
| 62 | Uruguay | NaturalistaUY | 4,041 |
| 63 | Bulgaria | 3,473 | |
| 64 | Nepal | 3,177 | |
| 65 | Jamaica | 3,138 | |
| 66 | Nicaragua | 2,966 | |
| 67 | Kazakhstan | 2,958 | |
| 68 | El Salvador | 2,832 | |
| 69 | Cuba | 2,707 | |
| 70 | Estonia | 2,686 | |
| 71 | Cyprus | 2,437 | |
| 72 | Serbia | 2,409 | |
| 73 | Cambodia | 2,407 | |
| 74 | Trinidad and Tobago | 2,326 | |
| 75 | Latvia | 2,273 | |
| 76 | Venezuela | 2,197 | |
| 77 | Montenegro | 2,118 | |
| 78 | Albania | 1,842 | |
| 79 | Malta | 1,672 | |
| 80 | Fiji | 1,650 | |
| 81 | Maldives | 1,568 | |
| 82 | Pakistan | 1,509 | |
| 83 | Bhutan | 1,452 | |
| 84 | Bosnia and Herzegovina | 1,404 | |
| 85 | Iran | 1,404 | |
| 86 | Armenia | 1,339 | |
| 87 | Saudi Arabia | 1,330 | |
| 88 | Paraguay | 1,280 | |
| 89 | Myanmar | 1,260 | |
| 90 | Jordan | 1,255 | |
| 91 | Laos | 1,254 | |
| 92 | Mongolia | 1,241 | |
| 93 | Macedonia | 1,100 | |
| 94 | Palestine | 1,093 | |
| 95 | Oman | 1,041 | |
| 96 | Uzbekistan | 969 | |
| 97 | Bangladesh | 904 | |
| 98 | Kyrgyzstan | 898 | |
| 99 | Lebanon | 703 | |
| 100 | Azerbaijan | 702 | |
| 101 | Papua New Guinea | 655 | |
| 102 | Guyana | 598 | |
| 103 | Cyprus | 532 | |
| 104 | Suriname | 485 | |
| 105 | Vanuatu | 478 | |
| 106 | Qatar | 461 | |
| 107 | Haiti | 436 | |
| 108 | Palau | 414 | |
| 109 | Moldova | 388 | |
| 110 | Iraq | 377 | |
| 111 | Liechtenstein | 375 | |
| 112 | Brunei | 346 | |
| 113 | Federated States of Micronesia | 345 | |
| 114 | Kuwait | 326 | |
| 115 | Tajikistan | 300 | |
| 116 | Georgia | 293 | |
| 117 | Georgia | 293 | |
| 118 | Bahrain | 291 | |
| 119 | Solomon Islands | 255 | |
| 120 | Samoa | 202 | |
| 121 | Syria | 172 | |
| 122 | Tonga | 168 | |
| 123 | Yemen | 129 | |
| 124 | Afghanistan | 128 | |
| 125 | Marshall Islands | 88 | |
| 126 | Turkmenistan | 84 | |
| 127 | North Korea | 76 | |
| 128 | Kiribati | 35 | |
| 129 | Tuvalu | 24 | |
| 130 | Nauru | 11 | |
| 131 | Timor Leste |
## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(pop/100000, n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Population of the country (hundred thousand)',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in GBIF
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(pop/100000, n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Population of the country (hundred thousand)',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(pop/100000, n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', col= 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Population of the country (hundred thousand)',
y='Number of users recording on iNaturalist') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(area/1000,n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Area of the country (thousand km2)',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in gbif
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(area/1000, n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Area of the country (thousand km2)',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(area/1000, n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Area of the country (thousand km2)',
y='Number of users recording on iNaturalist') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(abs(latitude), n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Absolute decimal latitude of the country\'s centroid',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in gbif
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(abs(latitude), n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Absolute decimal latitude of the country\'s centroid',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>%
mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(abs(latitude), n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Absolute decimal latitude of the country\'s centroid',
y='Number of users recording on iNaturalist') +
#scale_x_log10() +
scale_y_log10() +
theme_bw()## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp/1000, n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP per capita (thousand USD)',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in gbif
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp/1000, n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP per capita (thousand USD)',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>%
mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp/1000, n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP per capita (thousand USD)',
y='Number of users recording on iNaturalist') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp_research, n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP of the country dedicated to research (%)',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in gbif
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp_research, n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP of the country dedicated to research (%)',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>%
mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp_research,n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP of the country dedicated to research (%)',
y='Number of users recording on iNaturalist') +
scale_x_log10() + scale_y_log10() +
theme_bw()data_regressions <- data_variables %>%
mutate(has_site = ifelse(!is.na(site_name), 1, 0)) %>%
mutate(has_site = as.factor(has_site)) %>%
mutate(n_gbif_inat = n_records_gbif_iNat) %>%
dplyr::select(country_code, n_records_inat, n_gbif_inat, n_users,
area, gdp, gdp_research, pop, latitude, has_site) %>%
filter(!is.na(gdp_research) & !is.na(latitude)) # remove NAsfit_n_records <- lm(n_records_inat ~ area + gdp + gdp_research + pop + latitude + has_site, data=data_regressions)
step_n_records <- stepAIC(fit_n_records, direction = 'both')Start: AIC=2737.11
n_records_inat ~ area + gdp + gdp_research + pop + latitude +
has_site
Df Sum of Sq RSS AIC
- latitude 1 344312822342 200498268006804 2735.3
- gdp 1 2045694974235 202199650158697 2736.1
<none> 200153955184462 2737.1
- gdp_research 1 4787726758104 204941681942566 2737.4
- pop 1 8457944845656 208611900030118 2739.1
- has_site 1 34413380499980 234567335684442 2750.3
- area 1 228263719160992 428417674345454 2808.2
Step: AIC=2735.28
n_records_inat ~ area + gdp + gdp_research + pop + has_site
Df Sum of Sq RSS AIC
- gdp 1 2110933242008 202609201248812 2734.3
<none> 200498268006804 2735.3
- gdp_research 1 4512780289809 205011048296613 2735.4
+ latitude 1 344312822342 200153955184462 2737.1
- pop 1 9307240602392 209805508609196 2737.6
- has_site 1 41024377098196 241522645105000 2751.2
- area 1 231268468371831 431766736378636 2806.9
Step: AIC=2734.28
n_records_inat ~ area + gdp_research + pop + has_site
Df Sum of Sq RSS AIC
<none> 202609201248812 2734.3
+ gdp 1 2110933242008 200498268006804 2735.3
+ latitude 1 409551090115 202199650158697 2736.1
- pop 1 11918923289467 214528124538279 2737.8
- gdp_research 1 13825676456453 216434877705265 2738.6
- has_site 1 43407421408127 246016622656939 2750.9
- area 1 234531565047059 437140766295871 2806.1
step_n_records$anova # display results Stepwise Model Path
Analysis of Deviance Table
Initial Model:
n_records_inat ~ area + gdp + gdp_research + pop + latitude +
has_site
Final Model:
n_records_inat ~ area + gdp_research + pop + has_site
Step Df Deviance Resid. Df Resid. Dev AIC
1 89 200153955184462 2737.113
2 - latitude 1 344312822342 90 200498268006804 2735.278
3 - gdp 1 2110933242008 91 202609201248812 2734.283
fit_gbif <- lm(n_gbif_inat ~ area + gdp + gdp_research + pop + latitude + has_site, data=data_regressions)
step_gbif <- stepAIC(fit_gbif, direction = 'both')Start: AIC=2605.38
n_gbif_inat ~ area + gdp + gdp_research + pop + latitude + has_site
Df Sum of Sq RSS AIC
- latitude 1 62180598692 50810539623647 2603.5
- gdp 1 880881687111 51629240712065 2605.0
<none> 50748359024954 2605.4
- gdp_research 1 1213101818248 51961460843203 2605.7
- pop 1 5114018286268 55862377311223 2612.6
- has_site 1 8264345232892 59012704257846 2617.9
- area 1 58206567346013 108954926370968 2676.7
Step: AIC=2603.5
n_gbif_inat ~ area + gdp + gdp_research + pop + has_site
Df Sum of Sq RSS AIC
- gdp 1 864881071670 51675420695316 2603.1
<none> 50810539623647 2603.5
- gdp_research 1 1325908073822 52136447697469 2604.0
+ latitude 1 62180598692 50748359024954 2605.4
- pop 1 5069702406808 55880242030454 2610.6
- has_site 1 8727279860165 59537819483812 2616.7
- area 1 58286225002851 109096764626498 2674.9
Step: AIC=2603.12
n_gbif_inat ~ area + gdp_research + pop + has_site
Df Sum of Sq RSS AIC
<none> 51675420695316 2603.1
+ gdp 1 864881071670 50810539623647 2603.5
+ latitude 1 46179983251 51629240712065 2605.0
- gdp_research 1 4498270324371 56173691019687 2609.1
- pop 1 6330677482152 58006098177468 2612.2
- has_site 1 9397524358271 61072945053588 2617.2
- area 1 59283775386974 110959196082290 2674.5
step_gbif$anova # display results Stepwise Model Path
Analysis of Deviance Table
Initial Model:
n_gbif_inat ~ area + gdp + gdp_research + pop + latitude + has_site
Final Model:
n_gbif_inat ~ area + gdp_research + pop + has_site
Step Df Deviance Resid. Df Resid. Dev AIC
1 89 50748359024954 2605.381
2 - latitude 1 62180598692 90 50810539623647 2603.499
3 - gdp 1 864881071670 91 51675420695316 2603.119
fit_users <- lm(n_users ~ area + gdp + gdp_research + pop + latitude + has_site, data=data_regressions)
step_users <- stepAIC(fit_users, direction = 'both')Start: AIC=1969.88
n_users ~ area + gdp + gdp_research + pop + latitude + has_site
Df Sum of Sq RSS AIC
- gdp 1 446227000 68130407575 1968.5
- pop 1 1009518167 68693698741 1969.3
<none> 67684180574 1969.9
- latitude 1 2070063477 69754244051 1970.8
- gdp_research 1 2955717612 70639898187 1972.0
- has_site 1 7833024823 75517205397 1978.4
- area 1 35455856719 103140037294 2008.3
Step: AIC=1968.51
n_users ~ area + gdp_research + pop + latitude + has_site
Df Sum of Sq RSS AIC
- pop 1 1370108675 69500516250 1968.4
<none> 68130407575 1968.5
- latitude 1 2144472323 70274879897 1969.5
+ gdp 1 446227000 67684180574 1969.9
- gdp_research 1 6907906387 75038313962 1975.8
- has_site 1 8212255259 76342662834 1977.4
- area 1 35984618270 104115025845 2007.2
Step: AIC=1968.42
n_users ~ area + gdp_research + latitude + has_site
Df Sum of Sq RSS AIC
<none> 69500516250 1968.4
+ pop 1 1370108675 68130407575 1968.5
+ gdp 1 806817509 68693698741 1969.3
- latitude 1 2901754605 72402270855 1970.3
- gdp_research 1 6704298637 76204814886 1975.3
- has_site 1 9125357394 78625873644 1978.3
- area 1 37630371847 107130888096 2008.0
step_users$anova # display results Stepwise Model Path
Analysis of Deviance Table
Initial Model:
n_users ~ area + gdp + gdp_research + pop + latitude + has_site
Final Model:
n_users ~ area + gdp_research + latitude + has_site
Step Df Deviance Resid. Df Resid. Dev AIC
1 89 67684180574 1969.882
2 - gdp 1 446227000 90 68130407575 1968.513
3 - pop 1 1370108675 91 69500516250 1968.424
# n_records_inat ~ area + gdp_research + pop + has_site
# n_gbif_inat ~ area + gdp_research + pop + has_site
# n_users ~ area + gdp_research + latitude + has_site
modelo_n_records <- lm(n_records_inat ~ area + gdp_research + pop + has_site, data=data_regressions)
modelo_gbif <- lm(n_gbif_inat ~ area + gdp_research + pop + has_site, data=data_regressions)
modelo_users <- lm(n_users ~ area + gdp_research + latitude + has_site, data=data_regressions)stargazer::stargazer(modelo_n_records,
ci = T, digits=1,
type='html',
title = 'Número de registros en iNaturalist')| Dependent variable: | |
| n_records_inat | |
| area | 0.8*** |
| (0.7, 1.0) | |
| gdp_research | 328,600.8** |
| (70,147.3, 587,054.3) | |
| pop | -0.002** |
| (-0.004, -0.000) | |
| has_site1 | 1,763,947.0*** |
| (980,949.0, 2,546,944.0) | |
| Constant | -95,408.5 |
| (-523,008.3, 332,191.3) | |
| Observations | 96 |
| R2 | 0.7 |
| Adjusted R2 | 0.6 |
| Residual Std. Error | 1,492,138.0 (df = 91) |
| F Statistic | 43.8*** (df = 4; 91) |
| Note: | p<0.1; p<0.05; p<0.01 |
stargazer::stargazer(modelo_gbif,
ci = T, digits=1,
type='html',
title = 'Cantidad de registros en GBIF')| Dependent variable: | |
| n_gbif_inat | |
| area | 0.4*** |
| (0.3, 0.5) | |
| gdp_research | 187,433.9*** |
| (56,908.6, 317,959.3) | |
| pop | -0.001*** |
| (-0.002, -0.001) | |
| has_site1 | 820,748.6*** |
| (425,315.7, 1,216,182.0) | |
| Constant | -88,221.4 |
| (-304,169.7, 127,727.0) | |
| Observations | 96 |
| R2 | 0.6 |
| Adjusted R2 | 0.6 |
| Residual Std. Error | 753,566.0 (df = 91) |
| F Statistic | 41.9*** (df = 4; 91) |
| Note: | p<0.1; p<0.05; p<0.01 |
stargazer::stargazer(modelo_users,
ci = T, digits=1,
type='html',
title = 'Cantidad de usuarios en iNat')| Dependent variable: | |
| n_users | |
| area | 0.01*** |
| (0.01, 0.01) | |
| gdp_research | 7,324.0*** |
| (2,479.0, 12,168.9) | |
| latitude | -93.5* |
| (-187.4, 0.5) | |
| has_site1 | 26,909.3*** |
| (11,651.3, 42,167.4) | |
| Constant | 3,516.9 |
| (-4,688.3, 11,722.0) | |
| Observations | 96 |
| R2 | 0.5 |
| Adjusted R2 | 0.5 |
| Residual Std. Error | 27,635.9 (df = 91) |
| F Statistic | 25.5*** (df = 4; 91) |
| Note: | p<0.1; p<0.05; p<0.01 |